How Does This Translate to Science Type I Error
A Type I error is often referred to as a 'false positive', and is the process of incorrectly rejecting the null hypothesis in favor of the alternative. In the case above, the null hypothesis refers to the natural state of things, stating that the patient is not HIV positive.
The alternative hypothesis states that the patient does carry the virus. A Type I error would indicate that the patient has the virus when they do not, a false rejection of the null.
Type II Error
A Type II error is the opposite of a Type I error and is the false acceptance of the null hypothesis. A Type II error, also known as a false negative, would imply that the patient is free of HIV when they are not, a dangerous diagnosis.
In most fields of science, Type II errors are not seen to be as problematic as a Type I error. With the Type II error, a chance to reject the null hypothesis was lost, and no conclusion is inferred from a non-rejected null. The Type I error is more serious, because you have wrongly rejected the null hypothesis.
Medicine, however, is one exception; telling a patient that they are free of disease, when they are not, is potentially dangerous.
Replication
This is the reason why scientific experiments must be replicatable, and other scientists must be able to follow the exact methodology.
Even if the highest level of proof, where P < 0.01 (probability is less than 1%), is reached, out of every 100 experiments, there will be one false result. To a certain extent, duplicate or triplicate samples reduce the chance of error, but may still mask chance if the error causingvariable is present in all samples.
If however, other researchers, using the same equipment, replicate the experiment and find that the results are the same, the chances of 5 or 10 experiments giving false results is unbelievably small. This is how science regulates, and minimizes, the potential for Type I and Type II errors.
Of course, in non-replicatable experiments and medical diagnosis, replication is not always possible, so the possibility of Type I and II errors is always a factor.
One area that is guilty of ignoring Type I and II errors is the lawcourt, where the jury is not told that fingerprint and DNA tests may produce false results. There have been many documented miscarriages of justice involving these tests. Many courts will now not accept these tests alone, as proof of guilt, and require other evidence.
Type III Errors
Many statisticians are now adopting a third type of error, a type III, which is where the null hypothesis was rejected for the wrong reason.
In an experiment, a researcher might postulate a hypothesis and perform research. After analyzing the results statistically, the null is rejected.
The problem is, that there may be some relationship between thevariables, but it could be for a different reason than stated in the hypothesis. An unknown process may underlie the relationship.
Conclusion
Both Type I errors and Type II errors are factors that every scientist and researcher must take into account.
Whilst replication can minimize the chances of an inaccurate result, this is one of the major reasons why research should be replicatable.
Many scientists do not accept quasi-experiments, because they are difficult to replicate and analyze.
A Research Hypothesis
A research hypothesis is the statement created by researchers when they speculate upon the outcome of a research or experiment.
Every true experimental design must have this statement at the core of its structure, as the ultimate aim of any experiment.
The hypothesis is generated via a number of means, but is usually the result of a process of inductive reasoning where observations lead to the formation of a theory. Scientists then use a large battery ofdeductive methods to arrive at a hypothesis that is testable, falsifiable and realistic.
The precursor to a hypothesis is a problem, usually framed as a question.
The precursor to a hypothesis is a research problem, usually framed as a question. It might ask what, or why, something is happening.
For example, to use a topical subject, we might wonder why the stocks of cod in the North Atlantic are declining. The problem question might be ‘Why are the numbers of Cod in the North Atlantic declining?’
This is too broad as a statement and is not testable by any reasonable scientific means. It is merely a tentative question arising from literature reviews and intuition. Many people would think that instinct and intuition are unscientific, but many of the greatest scientific leaps were a result of ‘hunches’.
The research hypothesis is a paring down of the problem into something testable and falsifiable. In the aforementioned example, a researcher might speculate that the decline in the fish stocks is due to prolonged over fishing. Scientists must generate a realistic and testable hypothesis around which they can build the experiment.
This might be a question, a statement or an ‘If/Or’ statement. Some examples could be:
Is over-fishing causing a decline in the stocks of Cod in the North Atlantic?
Over-fishing affects the stocks of cod.
If over-fishing is causing a decline in the numbers of Cod, reducing the amount of trawlers will increase cod stocks.
These are all acceptable statements and they all give the researcher a focus for constructing a research experiment. Science tends to formalize things and use the ‘If’ statement, measuring the effect that manipulating one variable has upon another, but the other forms are perfectly acceptable. An ideal research hypothesis should contain a prediction, which is why the more formal ones are favored.
A hypothesis must be testable, but must also be falsifiable for its acceptance as true science.
A scientist who becomes fixated on proving a research hypothesis loses their impartiality and credibility. Statistical tests often uncover trends, but rarely give a clear-cut answer, with other factors often affecting the outcome and influencing the results.
Whilst gut instinct and logic tells us that fish stocks are affected by over fishing, it is not necessarily true and the researcher must consider that outcome. Perhaps environmental factors or pollution are causal effects influencing fish stocks.
A hypothesis must be testable, taking into account current knowledge and techniques, and be realistic. If the researcher does not have a multi-million dollar budget then there is no point in generating complicated hypotheses. A hypothesis must be verifiable by statistical and analytical means, to allow a verification or falsification.
In fact, a hypothesis is never proved, and it is better practice to use the terms ‘supported’ or ‘verified’. This means that the research showed that the evidence supported the hypothesis and further research is built upon that.
A research hypothesis, which stands the test of time, eventually becomes a theory, such as Einstein’s General Relativity. Even then, as with Newton’s Laws, they can still be falsified or adapted.
The Null Hypothesis
The null hypothesis, H0, is an essential part of any research design, and is always tested, even indirectly.
The simplistic definition of the null is as the opposite of the alternative hypothesis, H1, although the principle is a little more complex than that.
The null hypothesis (H0) is a hypothesis which the researcher tries to disprove, reject or nullify.
The 'null' often refers to the common view of something, while the alternative hypothesis is what the researcher really thinks is the cause of a phenomenon.
An experiment conclusion always refers to the null, rejecting or accepting H0 rather than H1.
Despite this, many researchers neglect the null hypothesis whentesting hypotheses, which is poor practice and can have adverse effects.